Wirtschaft (MSB)
Refine
Year
- 2020 (67) (remove)
Publication Type
- Article (26)
- Conference Proceeding (14)
- Part of a Book (10)
- Contribution to a Periodical (7)
- Book (5)
- Lecture (2)
- Working Paper (2)
- Report (1)
Keywords
- Abmahnung (1)
- Alternative Wirtschaftssysteme (1)
- Augmented Reality (1)
- B2 (1)
- Bewertungsgesetz (1)
- Bilanzanalyse (1)
- Bilanzpolitik (1)
- Continuous Software Engineering (1)
- Corona (1)
- Cost drivers (1)
§ 296 HGB
(2020)
§ 294 HGB
(2020)
Unser Wirtschaftssystem stößt an planetarische Grenzen, wie beispielsweise durch den immer schneller voranschreitenden menschgemachten Klimawandel deutlich wird. Es stellt sich die Frage, ob das auch anders geht: Wie kann ein Wirtschaftssystem aussehen, dass mit den Grenzen unseres Erdsystems kompatibel ist? Welche Ansätze gibt es und welche werden bereits praktisch umgesetzt? Kann das funktionieren, ohne dass unser Wohlstand abnimmt?
A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, regulators demand these models to be transparent and auditable. Thus, in credit scoring, very simple predictive models such as logistic regression or decision trees are still widely used and the superior predictive power of modern machine learning algorithms cannot be fully leveraged. Significant potential is therefore missed, leading to higher reserves or more credit defaults. This paper works out different dimensions that have to be considered for making credit scoring models understandable and presents a framework for making ``black box'' machine learning models transparent, auditable and explainable. Following this framework, we present an overview of techniques, demonstrate how they can be applied in credit scoring and how results compare to the interpretability of score cards. A real world case study shows that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power.